usingan alytics to address high impact business priorities inhospitality 130903055346-phpapp02

© AbsolutData 2013 Proprietary & Confidential

Using Analytics to address high impact business priorities in Hospitality

Examples Demonstrating Potential Impact

Guha Athreya

Hospitality Analytics Expert

AbsolutData

By:


Agenda

2

Analytics in Hospitality

Success Stories

Marketing Mix Optimization

New Hotel Location Decisions

CRM Strategy

Guest Satisfaction


CXOs in the Hospitality industry are looking for

Answers to Critical Business Questions

3

Which new

properties and

locations should

we invest in?

How do we

maximize

REVPAR?

How do we

enhance flow

through to the

bottom line?

How do we

maximize

customer value? How can we

optimize

Marketing ROI?


These questions can be answered through

analytics

4

Concept Testing & Product DesignWhich new properties

and locations should we

invest in?

Understand central delivery capacity to markets

Assess impact of new branded and un-branded supplies

Concept Testing & Product DesignHow do we maximize

REVPAR?

Understand price sensitivity to demand

Forecast demand at various price points

Identify opportunities for higher opaque pricing

Concept Testing & Product DesignHow do we enhance flow

through to the bottom

line?

Offer discounts only when there is a clear incremental impact

Move customers to profitable booking channels

Identify acquisition sources that bring in loyal, high value customers

Concept Testing & Product DesignHow can we maximize

customer value?

Measure Wallet Size to identify high potential customers

Identify opportunities to engage, up sell, cross sell, prevent attrition and re-

engage

Develop customer centric programs that drive revenue and engagement

Concept Testing & Product DesignHow can we optimize

Marketing ROI?

Assess revenue that is truly attributable to channels and marketing investments

Understand synergies, optimize spend and timing

Business Questions How Analytics can help


ROI on these efforts is High

FIVE PRE-REQUISITES FOR SUCCESS

Senior management buy-in

Integration of disparate data sources with enterprise wide access

Scalable insights delivery model

Test and learn culture

Collaboration across departments

5


Market Mix OptimizationOptimizing Market Mix to increase ROI by 25%

6

Business Concern

To find the True Value of reported impact in a multi

channel, multi value ecosystem.

Same numbers were being reported by multiple platforms.

The question was how to increase the effectiveness of the

platforms

how do we optimize marketing ROI across online and

off line channels?

Complications

Resolution Business Impact

30% 45% 60% 20% 100%

???

Phase 2- Refinements, e.g. Structural Equation Models

Assess Synergies, Refine Attribution

Phase 1- Holistic Base Model, Market Mix

Holistic approach incorporates all drivers with Appropriate

level of modelling sophistication - OLS, HB

Phase 3 – Triangulation e.g. Cookie Data

Cookie data captures Unique ID activity and measure

recency and frequency

Attribution’s % impact of each media channel drives daily

proportions

53.1%

20.5%

14.1%

6.0%4.1% 2.9% 0%

23%

45.6%

12.3%8.8%

5.5% 4.5%0.1%

0%

10%

20%

30%

40%

50%

60%

Search TV Affiliates Display PR E-mail Print

Primary Attribution

Impact of Secondary

Relationship

on Search= - 30%

Impact of

Secondary

Relationship

on TV= +25%


New Hotel Location DecisionsReducing error rate of predicting size of wallet by 20 %

7

Business Concern

They also wanted to predict the wallets for stays at client’s

hotels versus all competitors, with lower error rate (higher

on accuracy)

The Client needed to take the decision of where to locate

its new hotels, and their estimated ROI

Complications


Overlaid the untapped opportunity (1-share of wallet) to

identify opportunity spaces – using past stay behaviour

from C360

Identified destinations which a customer is likely to visit

based on his/her geographic and demographic profile –

requires external data e.g. Visa to assess historical size of

wallet

Identified customers that are most likely to stay

incrementally at the proposed new property – using

behavioural and demographic information from C360.

The actual size of the wallet (point estimate) was predicted

with a reduction in the error rate by 20%

50% improvement in the accuracy rate in classifying guests

into the High/Medium/Low Categories

The predicted point estimates target customers with the

highest potential

No data of existing market

Reliance on competitor data which is usually inaccessible

and sometimes, inaccurate


CRM StrategyTargeted Promotions impacts profits by over $100mn

8

Business Concern

In an ever increasing loyalty customer base, the client

needed to scientifically decide which promotion should be

sent to which customer, at what time, and using which

channel

Complications


Email is reaching Saturation

The loyal customers get blasted with promotions resulting

in opt-outs, while the non-loyal ones do not receive them

leaving money on the table

In the email world, the promotional & communication

calendar is managed by moving the offer schedules, a

process that does not truly address underlying conflict or

optimize value.

Unified Metrics

Financial Metrics

Engagement Indices

Quantifying Value

Response

Short Term Value

Incremental Value

Long Term Engagement Impact

Optimize Algorithms

Prioritized Contacts

Constraints

Go / No Go Decisions

Test & Learn

Enhance Offers

Improve Targeting

Enhance Business Rules

Synergies with other direct marketing initiatives to develop

new vehicles

Combined Impact expected to exceed $100 MN over12

months


Guest SatisfactionStreamlining focus areas and prioritizing action points

increased guest retainership by 51%

9

Business Concern

Satisfaction Scores were Dropping for a lot of client

property. They wanted to know why, and ways to mitigate it

Complications


Specific focus areas were identified and shared with property

managers to

Help them manage their assets better

Prioritize key action points to improve customer

satisfaction

PROPERTY

LEVEL

ANALYSIS

DATABASE

Internal Database(Customer mix, Staffing Levels)

Survey Information(Satisfaction and Loyalty scores)

Trade Audit Data( Competitor syndicated data, relative satisfaction scores)

Data sources disparate from each other

Best

Better

Good

Average

Low

Each property was moved from its

current performance band to next higher

band

Multiply the target movements with pre-

determined %increments

40% improvement in customer satisfaction

51% guests retained by implementing the

findings

9

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usingan alytics to address high impact business priorities inhospitality 130903055346-phpapp02

Business

proprietary confidential

impact of new branded

business concern

proprietary confidential

marketing roi

customer value

true value of reported

new properties